Electronic commerce has steadily grown as online retailers have become better able to provide customers with a service that rivals the traditional shopping experience. Online retailers can offer the convenience of shopping from home, fast shipping and flexible return policies, as well as an enormous selection of products. However, this scale of inventory can be hundreds of times larger than what is ordinarily stocked in a brick-and-mortar store and poses a unique challenge to online retailers: it is practically impossible for a customer to explore all of the options.
For many types of merchandise, such as soft goods (e.g. apparel, shoes, accessories), customers' purchase decisions are heavily influenced by the how the merchandise looks. Unfortunately, traditional web retail interfaces employ text-based searching, and do not make use of the highly relevant information contained in images. Techniques from the field of content-based image retrieval can be applied to extract characteristic information from each image and determine the visual similarity between pairs of images. A common operation is to identify the set of images which are visually similar to a given query image.
Several applications of content-based image retrieval have been designed for the World Wide Web. Search engines such as Google Images or Gazopa attempt to index all of the publicly available image data Comparison shopping engines such as Pixsta or Like.com are more specifically targeted to (real-time) searching images of merchandise in the context of electronic commerce.
Searching via image content enables customers to sift through large inventories to find specific items. However, the search paradigm has limitations: many customers shopping for soft goods may not know how to characterize what they are searching for, and many more do not even have a precise idea of what it is they want. Instead, they prefer to browse, identifying characteristics of items they like and discovering items with similar characteristics that might satisfy their criteria for purchase. This model of product discovery is not well-supported by existing visual search interfaces.